Mohanlal Sukhadia University also called University of Udaipur is a public university in Udaipur city in Indian state of Rajasthan. It consists of four constituent colleges and 60 affiliated colleges from the districts of Chittorgarh, Rajsamand, Sirohi and Udaipur. The earlier agricultural university was turned into a multi-faculty university in 1964 and named university of Udaipur. In 1984 it was renamed as Mohanlal Sukhadia University in memory of politician Mohanlal Sukhadia. University has two campuses spread over an area of more than 600 acres of land. Lastly, University was accredited at "A" Grade by NAAC Bengaluru, with a CGPA of 3.11.
Abiotic stresses such as drought, salinity, extreme temperature, nutrient deficiency, heavy metals, and flooding significantly threaten global agricultural productivity by disrupting plant physiological, biochemical, and molecular processes. Early and accurate detection of these stresses is crucial for minimising yield losses; however, traditional monitoring approaches are often slow, subjective, and unable to capture subtle pre-symptomatic changes. Recent advances in artificial intelligence (AI) have transformed plant stress research by enabling rapid, data-driven analysis of complex biological signals. This review synthesizes current progress in AI-driven plant stress detection, highlighting the integration of machine learning and deep learning algorithms with advanced detectors and sensing platforms, including hyperspectral imaging (HSI), thermal cameras, Internet of Things (IoT)-based soil and water sensors, nanoscale biosensors, wearable plant devices, and remote sensing systems such as drones and satellites. These AI-powered technologies allow continuous and non-invasive monitoring of plant health, providing insights into stress-specific signatures associated with various abiotic stresses. This study evaluates the advantages of AI-based systems, such as early detection, high-throughput phenotyping, and real-time decision support, alongside prevailing challenges related to data standardization, model interpretability, environmental variability, and accessibility for low-resource farming systems. Finally, future perspectives are discussed for the potential of multimodal data fusion, digital twins, edge AI devices, and AI-integrated breeding pipelines to enhance crop resilience in a changing climate. Overall, this review demonstrates how AI is revolutionising plant stress diagnostics and paving the way for sustainable, predictive, and precision agriculture.
Air quality plays a pivotal role in regulating plant productivity and ecological stability. However, rapid urbanization and escalating vehicular emissions have severely altered atmospheric composition, exposing plants to intense oxidative, metabolic, and physiological stresses. Using the moss Semibarbula orientalis as a sensitive bioindicator, this study examines the mechanistic progression of plant responses along an air pollution gradient through chlorophyll a fluorescence, biochemical measurements, and multivariate statistical analyses. Under control conditions (S1), photosynthetic and metabolic attributes remained stable, reflecting optimal redox balance. Moderate pollution (S2) induced a compensatory phase marked by increased antioxidant enzyme activity and starch accumulation, indicating activation of redox defence pathways, while declining total soluble protein levels and early suppression of fluorescence parameters signalled initial photochemical stress. At S3 (Poor pollution), PSII performance indices and quantum yields declined sharply, coupled with intensified non-photochemical quenching and progressive reaction-centre closure (↓RC/CSm). Simultaneous reductions in antioxidant activity and carbohydrate reserves indicated a shift from acclimation to metabolic fatigue. At S4 (very Poor pollution), caused marked pigment degradation and depletion of starch and total soluble protein pools, revealing profound metabolic impairment. Inhibition of electron transport beyond QA⁻ [↓PSI₀, ↓PHI(Eo)], reduced primary photochemistry [↓PHI(Po)], and elevated energy dissipation [↑DIo/RC, ↑PHI(Do), ↑Kn] confirmed severe disruption of photosynthetic electron flow. Together, the pollution-gradient responsive biochemical and physiological alterations, including total chlorophyll, Fm, RC/CSm, PSI₀, PHI(P₀), PIabs, and PIcsm, establish these parameters as key diagnostic indicators of air-pollution stress, collectively demonstrating Semibarbula orientalis to be a highly sensitive and reliable bioindicator species for urban air-quality monitoring and ecological risk assessment.
In the advent of rapid climate change, the growing complexity of abiotic stress combinations poses a significant threat to global agricultural production and food security. Traditional approaches, such as univariate statistical models or isolated omics studies that focus on single stressors, are often inadequate for predicting crop performance in the contemporary multivariate stress conditions present in agriculture. Recent technological breakthroughs in high-throughput multi-omics, phenomics, and environmental monitoring have generated enormous datasets that clarify the intricate interplay between genetic and environmental factors affecting plant stress responses. Concurrently, machine learning (ML) and artificial intelligence (AI) methodologies have emerged as powerful tools for modeling these complex interactions; yet, their conventional “black box” nature limits biological interpretability and practical use in crop improvement. This review highlights recent developments in interpretable machine learning algorithms that anticipate multifactorial abiotic stress responses in climate-resilient crops. We analyze the integration of multi-omics data with high-throughput phenotyping and environmental factors using interpretable models, such as attention-based neural networks, SHAP value analysis, and decision tree ensembles. These techniques aim to enhance the predictive accuracy and clarify essential regulatory pathways and biological drivers influencing stress resilience. Additionally, we also shed light on the challenges in data integration, model transparency, and the translational properties of computational discoveries into practical breeding methodologies. Finally, we propose future research directions aimed at refining these AI-driven frameworks to expedite the creation of crop varieties with enhanced tolerance to multiple stressors. This review emphasizes the game-changing capability of interpretable machine learning to close the gap between computational predictions and operational, field-level applicability in precision agriculture in a changing climate.
Artificial light at night (ALAN) is an increasingly significant environmental disturbance, as it disrupts natural light–dark cycles that regulate daily and seasonal physiological processes and phenological events of all organisms. The use of artificial lighting in urban areas is rapidly increasing each year due to the rising number of unregulated vehicles, as well as the widespread installation of decorative lights, digital advertising boards, and streetlights. The objective of this research was to determine the impacts of artificial light at night (ALAN) on various ornamental garden plants such as Dieffenbachia seguine, Lawsonia inermis, Alocasia cucullata, Cynodon dactylon and Dypsis lutescens through the analyses of chlorophyll fluorescence transients, specific and phenomenological energy fluxes, density of functional PSII RCs, quantum yields (Fv/Fm, ϕE0), non-photochemical quenching (Kn) and photochemical quenching (Kp), superoxide dismutase (SOD) activity, and concentrations of chlorophylls, malondialdehyde (MDA) and starch content. The results of the present study highlight that plant responses to ALAN vary among species. The present investigation demonstrates that D. lutescens and C. dactylon exhibit pronounced sensitivity to ALAN, whereas D. seguine, L. inermis, and A. cucullata display a comparatively higher degree of tolerance. These findings underscore the need to preferentially select ALAN-tolerant species for urban plantation programs to minimize the ecological consequences associated with light pollution. Moreover, the study identifies specific photosynthetic parameters (OJIP transients, ET/CS, RC/CS, Kp, Kn, and PICS) along with key biochemical indicators (SOD activity, MDA accumulation, and chlorophyll content) as reliable diagnostic markers for distinguishing ALAN-sensitive and ALAN-tolerant species, thereby supporting informed species selection for sustainable urban greening.
Large carnivores face increasing challenges in human-dominated landscapes, where conflict undermines both livelihoods and conservation goals. We investigated the patterns, drivers, and community perceptions of human-leopard conflict in and around Jaisamand Wildlife Sanctuary, southern Aravalli Hills, Rajasthan, India, between 2011 and 2024. We documented a total of 572 conflict incidents, largely livestock depredation (98.08%), with goats, cows and calves being most frequently targeted. Conflict occurred year-round, peaking at night in cattle sheds and households. Modeling identified elevation, land-use/land-cover, distance to sanctuary, and distance to human habitation (DTHH) as strong predictors of conflict probability, with highest risk in built-up and scrubland areas near village peripheries. Husbandry practices, including poorly constructed cattle sheds seem to be associated with increased livestock vulnerability, while coping strategies (e.g., night guarding) were largely ineffective. Only 31% of depredation cases were claimed for compensation, and approved payouts were significantly lower than actual losses, reinforcing economic grievances. Encouragingly, no retaliatory killings were reported, reflecting cultural coexistence toward wildlife. Surveys (n = 201) revealed that local attitude were close to neutral, with only a slight negative tendency (mean attitude score = -0.2). Education level emerged as the strongest determinant of coexistence, with more educated respondents expressing greater positive orientation toward leopards. Our findings demonstrate that conflict in semi-arid landscapes arises from the intersection of ecological, socio-economic, and institutional factors. Effective coexistence strategies should prioritize leopard-proof livestock enclosures, equitable and timely compensation, education-based awareness programs, and landscape-level planning beyond protected areas to foster long-term human-leopard coexistence.
